Research on the Computational Model and Algorithm Efficiency of New Quality Productivity Optimization

This paper proposes a productivity optimization model based on mathematical modeling, combined with an improved optimization algorithm, aiming to improve the computational efficiency and optimization effect of productivity. First, a multi-constraint and multi-objective mathematical model for new quality productivity optimization is established, which comprehensively considers multiple factors such as resource consumption, production efficiency, and cost. The classical GA and PSO are applied in this paper, and an improved hybrid algorithm is presented to increase the convergence speed and the precision of the solution. The results of different algorithms are proved to be effective in the real productivity optimization. The results show that the improved hybrid algorithm shows high computational efficiency and accuracy in multiple experimental scenarios, and the optimization effect is significantly better than the traditional single algorithm. The experiment results indicate that the efficiency of productivity can be increased by 15%, and it can be used to improve the productivity effectively.